A data labeling pipeline is an end-to-end workflow for labeling a dataset. It includes the process for selecting and training the annotators, managing data labeling, and aggregating and verifying the results. As an output, it provides high-quality labeled data.

How much does it cost?

The pipeline itself is free to use. You only pay for labeling your data in Toloka. To try it for free, you can cover the cost of data labeling with the promo code provided in our tutorial .

Are there any limitations on dataset size?

You can label a dataset of any size. The pipeline fits any project where you need to detect objects in images.

I need the labeled images to have several classes. Is this possible?

If you only have a few classes, you can run labeling for each class separately. If you have too many classes to label in parallel, feel free to contact us . We can help you tune the pipeline to your needs.

Does the pipeline only work in Toloka?

Yes, it is made specifically to get data labeled in Toloka via the Toloka API.

How does the pipeline ensure the quality of labeled data?

In a nutshell, the quality is ensured by overlap (several people doing the same task) and a separate stage of verification. See our tutorial to learn more.

Object detection pipeline

Run Toloka's object detection pipeline to get a human-labeled dataset.
No need to set up the image labeling process yourself.

Upload your unlabeled image files to any cloud storage
Sign up for Toloka as a requester
Run the Python code from our Jupyter Notebook tutorial
To try it all for free, use our promo code

The pipeline
works well
for any object
detection task

Our tutorial shows how to apply the pipeline to your dataset, using the face recognition task as an example.

Retail and e-commerce
- Product recognition on store shelves
- Virtual fitting rooms
- People counting for retail stores
Transportation
- Pedestrian detection
- Traffic prediction
- Parking occupancy detection
- Road condition monitoring
ManufacturingPersonal protective equipment detection
Biometrics
- Facial feature detection
- Iris recognition
Agriculture
- Plant disease detection
- Object detection in agriculture
MarketingsLogo recognition

Benefits of using a pipeline

Get high-quality labeled data
Toloka provides multiple tools to ensure quality of data labeling (overlap, control tasks, and more). The pipeline comes with these
tools already set up, and includes a stage dedicated specifically
to the results verification.
Cut time to production
You don't need to set up the labeling process from scratch.
Just use the pipeline and get ground truth data to
improve your ML model.
Automate the process
The pipeline contains Python code which automates the labeling process via the Toloka API. You can use it as-is for face detection
tasks. If you need to detect any other objects, we will show you
where and how to tune it to fit your task.
Fine-tune the pipeline to fit your needs
Our pipeline gives you a quick start, but you can still access all project settings. Tweak it as much and as often as you need to. Our support team can help you along the way.

Success stories

For more than 10 years, Toloka's clients have been successfully solving business challenges. Here are some examples:

Face detection

A startup from Japan labeled 65,000 faces
in 3 weeks, at a fraction of the expected cost.

Learn more

Surrounding objects detection

A self-driving car project uses Toloka to label tens of thousands of mages for training a neural network to detect surrounding objects in an urban environment.

Learn more

License plate detection

Toloka's object detection template has been successfully used to create training datasets for license plate detection models for a parking management system.

Learn more